{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7a1bd7ef",
   "metadata": {},
   "source": [
    "# GRN Inference on Pre-trained Model\n",
    "Here we use the pre-trained blood model as an example for GRN inference, particularly regarding gene program extraction and network visualization. We also present the cell-type specific activations within these gene programs on the Immune Human dataset, as a soft validation for the zero-shot performance. \n",
    "\n",
    "Note that GRN inference can be performed on pre-trained and finetuned models as showcased in our manuscript.\n",
    "\n",
    "Users may perform scGPT's gene-embedding-based GRN inference in the following steps:\n",
    "\n",
    "     1. Load optimized scGPT model (pre-trained or fine-tuned) and data\n",
    "     \n",
    "     2. Retrieve scGPT's gene embeddings\n",
    "     \n",
    "     3. Extract gene programs from scGPT's gene embedding network\n",
    "     \n",
    "     4. Visualize gene program activations on dataset of interest\n",
    "     \n",
    "     5. Visualize the interconnectivity of genes within select gene programs\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "18b08bed",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Global seed set to 0\n",
      "/h/chloexq/.cache/pypoetry/virtualenvs/scgpt--qSLVbd1-py3.9/lib/python3.7/site-packages/flatbuffers/compat.py:19: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\n",
      "  import imp\n"
     ]
    }
   ],
   "source": [
    "import copy\n",
    "import json\n",
    "import os\n",
    "from pathlib import Path\n",
    "import sys\n",
    "import warnings\n",
    "\n",
    "import torch\n",
    "from anndata import AnnData\n",
    "import scanpy as sc\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import tqdm\n",
    "import gseapy as gp\n",
    "\n",
    "from torchtext.vocab import Vocab\n",
    "from torchtext._torchtext import (\n",
    "    Vocab as VocabPybind,\n",
    ")\n",
    "\n",
    "sys.path.insert(0, \"../\")\n",
    "import scgpt as scg\n",
    "from scgpt.tasks import GeneEmbedding\n",
    "from scgpt.tokenizer.gene_tokenizer import GeneVocab\n",
    "from scgpt.model import TransformerModel\n",
    "from scgpt.preprocess import Preprocessor\n",
    "from scgpt.utils import set_seed \n",
    "\n",
    "os.environ[\"KMP_WARNINGS\"] = \"off\"\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "948bbc49",
   "metadata": {},
   "outputs": [],
   "source": [
    "set_seed(42)\n",
    "pad_token = \"<pad>\"\n",
    "special_tokens = [pad_token, \"<cls>\", \"<eoc>\"]\n",
    "n_hvg = 1200\n",
    "n_bins = 51\n",
    "mask_value = -1\n",
    "pad_value = -2\n",
    "n_input_bins = n_bins"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9565a71c",
   "metadata": {},
   "source": [
    "## Step 1: Load pre-trained model and dataset"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "83ad0952",
   "metadata": {},
   "source": [
    "### 1.1  Load pre-trained model\n",
    "The blood pre-trained model can be downloaded via this [link](https://drive.google.com/drive/folders/1kkug5C7NjvXIwQGGaGoqXTk_Lb_pDrBU)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "86247948",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resume model from ../save/scGPT_bc/best_model.pt, the model args will override the config ../save/scGPT_bc/args.json.\n"
     ]
    }
   ],
   "source": [
    "# Specify model path; here we load the pre-trained scGPT blood model\n",
    "model_dir = Path(\"../save/scGPT_bc\")\n",
    "model_config_file = model_dir / \"args.json\"\n",
    "model_file = model_dir / \"best_model.pt\"\n",
    "vocab_file = model_dir / \"vocab.json\"\n",
    "\n",
    "vocab = GeneVocab.from_file(vocab_file)\n",
    "for s in special_tokens:\n",
    "    if s not in vocab:\n",
    "        vocab.append_token(s)\n",
    "\n",
    "# Retrieve model parameters from config files\n",
    "with open(model_config_file, \"r\") as f:\n",
    "    model_configs = json.load(f)\n",
    "print(\n",
    "    f\"Resume model from {model_file}, the model args will override the \"\n",
    "    f\"config {model_config_file}.\"\n",
    ")\n",
    "embsize = model_configs[\"embsize\"]\n",
    "nhead = model_configs[\"nheads\"]\n",
    "d_hid = model_configs[\"d_hid\"]\n",
    "nlayers = model_configs[\"nlayers\"]\n",
    "n_layers_cls = model_configs[\"n_layers_cls\"]\n",
    "\n",
    "gene2idx = vocab.get_stoi()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "19de9b6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using simple batchnorm instead of domain specific batchnorm\n",
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     ]
    },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading params transformer_encoder.layers.8.norm1.bias with shape torch.Size([512])\n",
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     ]
    },
    {
     "data": {
      "text/plain": [
       "TransformerModel(\n",
       "  (encoder): GeneEncoder(\n",
       "    (embedding): Embedding(36574, 512, padding_idx=36571)\n",
       "    (enc_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "  )\n",
       "  (value_encoder): ContinuousValueEncoder(\n",
       "    (dropout): Dropout(p=0.5, inplace=False)\n",
       "    (linear1): Linear(in_features=1, out_features=512, bias=True)\n",
       "    (activation): ReLU()\n",
       "    (linear2): Linear(in_features=512, out_features=512, bias=True)\n",
       "    (norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "  )\n",
       "  (bn): BatchNorm1d(512, eps=6.1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (transformer_encoder): TransformerEncoder(\n",
       "    (layers): ModuleList(\n",
       "      (0): TransformerEncoderLayer(\n",
       "        (self_attn): MultiheadAttention(\n",
       "          (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)\n",
       "        )\n",
       "        (linear1): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (dropout): Dropout(p=0.5, inplace=False)\n",
       "        (linear2): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (dropout1): Dropout(p=0.5, inplace=False)\n",
       "        (dropout2): Dropout(p=0.5, inplace=False)\n",
       "      )\n",
       "      (1): TransformerEncoderLayer(\n",
       "        (self_attn): MultiheadAttention(\n",
       "          (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)\n",
       "        )\n",
       "        (linear1): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (dropout): Dropout(p=0.5, inplace=False)\n",
       "        (linear2): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (dropout1): Dropout(p=0.5, inplace=False)\n",
       "        (dropout2): Dropout(p=0.5, inplace=False)\n",
       "      )\n",
       "      (2): TransformerEncoderLayer(\n",
       "        (self_attn): MultiheadAttention(\n",
       "          (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)\n",
       "        )\n",
       "        (linear1): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (dropout): Dropout(p=0.5, inplace=False)\n",
       "        (linear2): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (dropout1): Dropout(p=0.5, inplace=False)\n",
       "        (dropout2): Dropout(p=0.5, inplace=False)\n",
       "      )\n",
       "      (3): TransformerEncoderLayer(\n",
       "        (self_attn): MultiheadAttention(\n",
       "          (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)\n",
       "        )\n",
       "        (linear1): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (dropout): Dropout(p=0.5, inplace=False)\n",
       "        (linear2): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (dropout1): Dropout(p=0.5, inplace=False)\n",
       "        (dropout2): Dropout(p=0.5, inplace=False)\n",
       "      )\n",
       "      (4): TransformerEncoderLayer(\n",
       "        (self_attn): MultiheadAttention(\n",
       "          (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)\n",
       "        )\n",
       "        (linear1): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (dropout): Dropout(p=0.5, inplace=False)\n",
       "        (linear2): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (dropout1): Dropout(p=0.5, inplace=False)\n",
       "        (dropout2): Dropout(p=0.5, inplace=False)\n",
       "      )\n",
       "      (5): TransformerEncoderLayer(\n",
       "        (self_attn): MultiheadAttention(\n",
       "          (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)\n",
       "        )\n",
       "        (linear1): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (dropout): Dropout(p=0.5, inplace=False)\n",
       "        (linear2): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (dropout1): Dropout(p=0.5, inplace=False)\n",
       "        (dropout2): Dropout(p=0.5, inplace=False)\n",
       "      )\n",
       "      (6): TransformerEncoderLayer(\n",
       "        (self_attn): MultiheadAttention(\n",
       "          (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)\n",
       "        )\n",
       "        (linear1): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (dropout): Dropout(p=0.5, inplace=False)\n",
       "        (linear2): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (dropout1): Dropout(p=0.5, inplace=False)\n",
       "        (dropout2): Dropout(p=0.5, inplace=False)\n",
       "      )\n",
       "      (7): TransformerEncoderLayer(\n",
       "        (self_attn): MultiheadAttention(\n",
       "          (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)\n",
       "        )\n",
       "        (linear1): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (dropout): Dropout(p=0.5, inplace=False)\n",
       "        (linear2): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (dropout1): Dropout(p=0.5, inplace=False)\n",
       "        (dropout2): Dropout(p=0.5, inplace=False)\n",
       "      )\n",
       "      (8): TransformerEncoderLayer(\n",
       "        (self_attn): MultiheadAttention(\n",
       "          (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)\n",
       "        )\n",
       "        (linear1): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (dropout): Dropout(p=0.5, inplace=False)\n",
       "        (linear2): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (dropout1): Dropout(p=0.5, inplace=False)\n",
       "        (dropout2): Dropout(p=0.5, inplace=False)\n",
       "      )\n",
       "      (9): TransformerEncoderLayer(\n",
       "        (self_attn): MultiheadAttention(\n",
       "          (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)\n",
       "        )\n",
       "        (linear1): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (dropout): Dropout(p=0.5, inplace=False)\n",
       "        (linear2): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (dropout1): Dropout(p=0.5, inplace=False)\n",
       "        (dropout2): Dropout(p=0.5, inplace=False)\n",
       "      )\n",
       "      (10): TransformerEncoderLayer(\n",
       "        (self_attn): MultiheadAttention(\n",
       "          (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)\n",
       "        )\n",
       "        (linear1): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (dropout): Dropout(p=0.5, inplace=False)\n",
       "        (linear2): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (dropout1): Dropout(p=0.5, inplace=False)\n",
       "        (dropout2): Dropout(p=0.5, inplace=False)\n",
       "      )\n",
       "      (11): TransformerEncoderLayer(\n",
       "        (self_attn): MultiheadAttention(\n",
       "          (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)\n",
       "        )\n",
       "        (linear1): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (dropout): Dropout(p=0.5, inplace=False)\n",
       "        (linear2): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (dropout1): Dropout(p=0.5, inplace=False)\n",
       "        (dropout2): Dropout(p=0.5, inplace=False)\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (decoder): ExprDecoder(\n",
       "    (fc): Sequential(\n",
       "      (0): Linear(in_features=512, out_features=512, bias=True)\n",
       "      (1): LeakyReLU(negative_slope=0.01)\n",
       "      (2): Linear(in_features=512, out_features=512, bias=True)\n",
       "      (3): LeakyReLU(negative_slope=0.01)\n",
       "      (4): Linear(in_features=512, out_features=1, bias=True)\n",
       "    )\n",
       "  )\n",
       "  (cls_decoder): ClsDecoder(\n",
       "    (_decoder): ModuleList(\n",
       "      (0): Linear(in_features=512, out_features=512, bias=True)\n",
       "      (1): ReLU()\n",
       "      (2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "      (3): Linear(in_features=512, out_features=512, bias=True)\n",
       "      (4): ReLU()\n",
       "      (5): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "    )\n",
       "    (out_layer): Linear(in_features=512, out_features=1, bias=True)\n",
       "  )\n",
       "  (sim): Similarity(\n",
       "    (cos): CosineSimilarity()\n",
       "  )\n",
       "  (creterion_cce): CrossEntropyLoss()\n",
       ")"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "ntokens = len(vocab)  # size of vocabulary\n",
    "model = TransformerModel(\n",
    "    ntokens,\n",
    "    embsize,\n",
    "    nhead,\n",
    "    d_hid,\n",
    "    nlayers,\n",
    "    vocab=vocab,\n",
    "    pad_value=pad_value,\n",
    "    n_input_bins=n_input_bins,\n",
    ")\n",
    "\n",
    "try:\n",
    "    model.load_state_dict(torch.load(model_file))\n",
    "    print(f\"Loading all model params from {model_file}\")\n",
    "except:\n",
    "    # only load params that are in the model and match the size\n",
    "    model_dict = model.state_dict()\n",
    "    pretrained_dict = torch.load(model_file)\n",
    "    pretrained_dict = {\n",
    "        k: v\n",
    "        for k, v in pretrained_dict.items()\n",
    "        if k in model_dict and v.shape == model_dict[k].shape\n",
    "    }\n",
    "    for k, v in pretrained_dict.items():\n",
    "        print(f\"Loading params {k} with shape {v.shape}\")\n",
    "        model_dict.update(pretrained_dict)\n",
    "        model.load_state_dict(model_dict)\n",
    "\n",
    "model.to(device)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f5e23d36",
   "metadata": {},
   "source": [
    "### 1.2  Load dataset of interest\n",
    "The Immune Human dataset can be downloaded via this [link](https://figshare.com/ndownloader/files/25717328)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cec95b62",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify data path; here we load the Immune Human dataset\n",
    "data_dir = Path(\"../data\")\n",
    "adata = sc.read(\n",
    "    str(data_dir / \"Immune_ALL_human.h5ad\"), cache=True\n",
    ")  # 33506 × 12303\n",
    "ori_batch_col = \"batch\"\n",
    "adata.obs[\"celltype\"] = adata.obs[\"final_annotation\"].astype(str)\n",
    "data_is_raw = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8f1a521e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scGPT - INFO - Filtering genes by counts ...\n",
      "scGPT - INFO - Filtering cells by counts ...\n",
      "scGPT - INFO - Normalizing total counts ...\n",
      "scGPT - INFO - Subsetting highly variable genes ...\n",
      "scGPT - INFO - Binning data ...\n"
     ]
    }
   ],
   "source": [
    "# Preprocess the data following the scGPT data pre-processing pipeline\n",
    "preprocessor = Preprocessor(\n",
    "    use_key=\"X\",  # the key in adata.layers to use as raw data\n",
    "    filter_gene_by_counts=3,  # step 1\n",
    "    filter_cell_by_counts=False,  # step 2\n",
    "    normalize_total=1e4,  # 3. whether to normalize the raw data and to what sum\n",
    "    result_normed_key=\"X_normed\",  # the key in adata.layers to store the normalized data\n",
    "    log1p=data_is_raw,  # 4. whether to log1p the normalized data\n",
    "    result_log1p_key=\"X_log1p\",\n",
    "    subset_hvg=n_hvg,  # 5. whether to subset the raw data to highly variable genes\n",
    "    hvg_flavor=\"seurat_v3\" if data_is_raw else \"cell_ranger\",\n",
    "    binning=n_bins,  # 6. whether to bin the raw data and to what number of bins\n",
    "    result_binned_key=\"X_binned\",  # the key in adata.layers to store the binned data\n",
    ")\n",
    "preprocessor(adata, batch_key=\"batch\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f752d133",
   "metadata": {},
   "source": [
    "## Step 2: Retrieve scGPT's gene embeddings\n",
    "\n",
    "Note that technically scGPT's gene embeddings are data independent. Overall, the pre-trained foundation model contains 30+K genes. Here for simplicity, we focus on a subset of HVGs specific to the data at hand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6f190778",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Retrieve the data-independent gene embeddings from scGPT\n",
    "gene_ids = np.array([id for id in gene2idx.values()])\n",
    "gene_embeddings = model.encoder(torch.tensor(gene_ids, dtype=torch.long).to(device))\n",
    "gene_embeddings = gene_embeddings.detach().cpu().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5efa8903",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retrieved gene embeddings for 1173 genes.\n"
     ]
    }
   ],
   "source": [
    "# Filter on the intersection between the Immune Human HVGs found in step 1.2 and scGPT's 30+K foundation model vocab\n",
    "gene_embeddings = {gene: gene_embeddings[i] for i, gene in enumerate(gene2idx.keys()) if gene in adata.var.index.tolist()}\n",
    "print('Retrieved gene embeddings for {} genes.'.format(len(gene_embeddings)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1181bcd5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1173/1173 [00:00<00:00, 1365127.25it/s]\n"
     ]
    }
   ],
   "source": [
    "# Construct gene embedding network\n",
    "embed = GeneEmbedding(gene_embeddings)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "71b9e16d",
   "metadata": {},
   "source": [
    "## Step 3: Extract gene programs from gene embedding network"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0bc58b46",
   "metadata": {},
   "source": [
    "### 3.1  Perform Louvain clustering on the gene embedding network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "569ea02a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform Louvain clustering with desired resolution; here we specify resolution=40\n",
    "gdata = embed.get_adata(resolution=40)\n",
    "# Retrieve the gene clusters\n",
    "metagenes = embed.get_metagenes(gdata)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "38dad229",
   "metadata": {},
   "source": [
    "### 3.2  Filter on clusters with 5 or more genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c763acf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Obtain the set of gene programs from clusters with #genes >= 5\n",
    "mgs = dict()\n",
    "for mg, genes in metagenes.items():\n",
    "    if len(genes) > 4:\n",
    "        mgs[mg] = genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3671c64b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'46': ['ZNF683', 'JAKMIP1', 'LAG3', 'FKBP11', 'ZBP1'],\n",
       " '7': ['ZFP36', 'TNFAIP3', 'DUSP1', 'FOS', 'CD69', 'KLF6', 'PPP1R15A', 'JUN'],\n",
       " '34': ['YPEL4', 'TMEM86B', 'TMEM63B', 'CYB5R1', 'RUNDC3A', 'DNAJB2'],\n",
       " '25': ['YPEL3', 'TLE1', 'CXXC5', 'RBM38', 'ODC1', 'TFDP2'],\n",
       " '43': ['YOD1', 'FLCN', 'CPEB4', 'FBXO30', 'MTMR3'],\n",
       " '14': ['VSIG4', 'C1QA', 'C1QC', 'C1QB', 'ACP5', 'CD163', 'ABCG2'],\n",
       " '71': ['VPREB3', 'VPREB1', 'SOCS2', 'IGLL1', 'DNTT'],\n",
       " '17': ['UROD', 'CMAS', 'CDC27', 'MINPP1', 'CPOX', 'RFESD'],\n",
       " '58': ['UBE2O', 'FN3K', 'PIM1', 'PNP', 'TRIM58'],\n",
       " '47': ['TXK', 'SPOCK2', 'SKAP1', 'CD96', 'RNF125'],\n",
       " '22': ['TTN', 'ICA1L', 'KCNQ1OT1', 'BCL9L', 'ABCA5', 'GPR82'],\n",
       " '27': ['TSC22D1', 'MEG3', 'PTCRA', 'PGRMC1', 'MLH3', 'GRAP2'],\n",
       " '21': ['TRAT1', 'LEF1', 'RCAN3', 'BCL11B', 'TCF7', 'OXNAD1'],\n",
       " '0': ['TPX2',\n",
       "  'STMN1',\n",
       "  'UBE2C',\n",
       "  'MKI67',\n",
       "  'TOP2A',\n",
       "  'CCNB1',\n",
       "  'CDK1',\n",
       "  'TYMS',\n",
       "  'TK1',\n",
       "  'NUSAP1',\n",
       "  'BIRC5',\n",
       "  'PTTG1',\n",
       "  'CENPF',\n",
       "  'RRM2'],\n",
       " '8': ['TPM2', 'TPM1', 'TGFB1I1', 'CALD1', 'MYL9', 'MYH11', 'MYLK'],\n",
       " '15': ['TNRC6C', 'DHRS3', 'FBXO32', 'APBA2', 'CEP290', 'PLEKHA1'],\n",
       " '42': ['TNFSF10', 'DPYSL2', 'AP1S2', 'GLIPR1', 'FGL2'],\n",
       " '68': ['TNFRSF17', 'MZB1', 'DERL3', 'CD38', 'POU2AF1'],\n",
       " '4': ['TNFRSF13B',\n",
       "  'SNRPN',\n",
       "  'SLC4A10',\n",
       "  'FCRL2',\n",
       "  'SPINK2',\n",
       "  'CAPN12',\n",
       "  'CELA1',\n",
       "  'CPA5',\n",
       "  'LILRA4'],\n",
       " '12': ['TMOD1', 'SOX6', 'ART4', 'SPTB', 'AK1', 'OSBP2', 'KANK2'],\n",
       " '37': ['TMEM154', 'HVCN1', 'NCF1', 'CKAP4', 'POU2F2'],\n",
       " '29': ['TLR10', 'FGD2', 'HLA-DOB', 'FCRL5', 'CD180', 'MACROD2'],\n",
       " '26': ['TIMP1', 'S100A11', 'LGALS1', 'ANXA2', 'LGALS3', 'IGFBP7'],\n",
       " '45': ['TCL1A', 'FCRLA', 'CD22', 'CD72', 'CD19'],\n",
       " '67': ['TAGAP', 'PHACTR1', 'PMAIP1', 'NFKBID', 'CD83'],\n",
       " '59': ['SYTL2', 'SMAGP', 'LAIR2', 'DTHD1', 'NCALD'],\n",
       " '13': ['SYNE2', 'SYNE1', 'PIK3R1', 'OPTN', 'CBLB', 'RORA', 'PARP8'],\n",
       " '54': ['STYK1', 'SP140', 'LPAR5', 'FCRL3', 'PRKD2'],\n",
       " '11': ['STXBP2', 'SPI1', 'TKT', 'AIF1', 'PYCARD', 'COTL1', 'LST1'],\n",
       " '9': ['STRBP', 'TRAF5', 'GNB5', 'BACH2', 'CDCA7L', 'CHD7', 'AFF3'],\n",
       " '57': ['STRADB', 'ISCA1', 'CCS', 'NPRL3', 'PITHD1'],\n",
       " '1': ['STAT1',\n",
       "  'ISG15',\n",
       "  'CMPK2',\n",
       "  'IFIT2',\n",
       "  'IFIT1',\n",
       "  'IFI44L',\n",
       "  'RSAD2',\n",
       "  'IFI6',\n",
       "  'SAMD9',\n",
       "  'IFIT3',\n",
       "  'MX1',\n",
       "  'IRF7'],\n",
       " '55': ['SPON2', 'MYOM2', 'CLIC3', 'CHST2', 'SH2D1B'],\n",
       " '16': ['SPEF2', 'ECHDC2', 'DUOX1', 'CLUAP1', 'CCDC146', 'CLIC6'],\n",
       " '52': ['SLC38A5', 'KCNH2', 'POLE2', 'CERS3', 'DHRS13'],\n",
       " '51': ['SLC38A11', 'IL6', 'CA3', 'FAM177B', 'PTPRS'],\n",
       " '49': ['SH3BGRL2', 'PDZK1IP1', 'CTDSPL', 'PDGFA', 'CTTN'],\n",
       " '32': ['SEMA4F', 'CARNS1', 'ARHGAP12', 'XYLT2', 'RECK', 'CBFA2T2'],\n",
       " '61': ['FCGR3A', 'DOK2', 'CX3CR1', 'ABI3', 'RHOC'],\n",
       " '19': ['CCR10', 'SLAMF1', 'IL2RA', 'CCR4', 'CCR6', 'CTLA4'],\n",
       " '72': ['ZFP36L1', 'EGR1', 'NFKBIZ', 'RHOB', 'NR4A1'],\n",
       " '50': ['VMP1', 'GPCPD1', 'NAMPT', 'CD55', 'SOD2'],\n",
       " '5': ['PRSS57',\n",
       "  'AZU1',\n",
       "  'CLEC11A',\n",
       "  'CTSG',\n",
       "  'ELANE',\n",
       "  'MS4A3',\n",
       "  'MPO',\n",
       "  'RETN',\n",
       "  'RNASE2'],\n",
       " '65': ['LILRA5', 'LILRB2', 'GTPBP2', 'HCK', 'LRRC25'],\n",
       " '3': ['LCK', 'CD2', 'LTB', 'CD3E', 'CD3G', 'CD8B', 'CD8A', 'IL7R', 'CD3D'],\n",
       " '70': ['LBH', 'MGAT4A', 'RHOH', 'SLC38A1', 'STK17A'],\n",
       " '36': ['PICALM', 'FOXO3', 'QKI', 'CTNNB1', 'RAPGEF2'],\n",
       " '41': ['IL6ST', 'ZNF37A', 'ITGA6', 'ACTN1', 'PDK1'],\n",
       " '35': ['P2RX5', 'PNOC', 'PKIG', 'BLK', 'OSBPL10', 'RRAS2'],\n",
       " '30': ['ID3', 'GPM6B', 'BEX5', 'CRIP2', 'APOLD1', 'TSHZ2'],\n",
       " '2': ['HLA-DRB5',\n",
       "  'HLA-DQA1',\n",
       "  'HLA-DPB1',\n",
       "  'HLA-DRA',\n",
       "  'HLA-DMA',\n",
       "  'HLA-DQB1',\n",
       "  'CD74',\n",
       "  'HLA-DQA2',\n",
       "  'HLA-DRB1',\n",
       "  'HLA-DPA1'],\n",
       " '24': ['CLEC4A', 'NRG1', 'TMEM107', 'CLEC4D', 'PTCH2', 'S1PR3'],\n",
       " '10': ['GNG11', 'CLEC1B', 'DNASE1L3', 'ESAM', 'CLDN5', 'MYCT1', 'EGFL7'],\n",
       " '18': ['GLUL', 'SLC40A1', 'APOE', 'FABP5', 'CTSB', 'CTSD'],\n",
       " '31': ['GIMAP4', 'GIMAP7', 'ETS1', 'GIMAP6', 'SLFN5', 'DENND2D'],\n",
       " '40': ['FGR', 'TNFRSF1B', 'AOAH', 'AGTRAP', 'MYO1F'],\n",
       " '56': ['ZSCAN18', 'MAP9', 'ZCWPW1', 'PLXNA3', 'ZNF274'],\n",
       " '6': ['VMO1', 'LYPD2', 'LGALS3BP', 'AQP3', 'HES4', 'SCGB3A1', 'IRF9', 'CD59'],\n",
       " '64': ['PASK', 'GATA3', 'CD40LG', 'AP3M2', 'P2RY10'],\n",
       " '44': ['CYP4F3', 'ARG1', 'LCN2', 'HP', 'S100P'],\n",
       " '28': ['PTPN7', 'LYAR', 'TCOF1', 'GTF3C1', 'CMC1', 'MATK'],\n",
       " '33': ['HDC', 'CYTL1', 'CPA3', 'CLC', 'LGALS4', 'GATA2'],\n",
       " '66': ['DAB2', 'F13A1', 'MAF', 'PRDM1', 'LPAR6'],\n",
       " '39': ['IGSF6', 'CPVL', 'CFP', 'LY86', 'RAB32'],\n",
       " '69': ['APOBEC3A', 'IFITM3', 'MT1F', 'IFI27', 'MT2A'],\n",
       " '48': ['NELL2', 'CAMK4', 'ABLIM1', 'PDE3B', 'SATB1'],\n",
       " '62': ['PLBD1', 'GCA', 'NUP214', 'PGD', 'LTA4H'],\n",
       " '38': ['GP1BA', 'ARHGAP6', 'ITGA2B', 'SELP', 'RAB27B'],\n",
       " '23': ['CCL20', 'CCL8', 'CXCL1', 'CXCL3', 'OSM', 'CCL2'],\n",
       " '53': ['BTN3A1', 'CALCOCO1', 'RAB2B', 'OPA1', 'ATG4A'],\n",
       " '20': ['NFIL3', 'EGR3', 'NLRP3', 'OTUD1', 'PTGS2', 'TRIB1'],\n",
       " '60': ['RAB3IP', 'MXI1', 'PGM2L1', 'FHIT', 'UBE2H'],\n",
       " '63': ['MAL', 'MYC', 'PIM2', 'CISH', 'S1PR1']}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here are the gene programs identified\n",
    "mgs"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "dd6066cd",
   "metadata": {},
   "source": [
    "## Step 4: Visualize gene program activation on the Immune Human dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "078a3233",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 360x936 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x648 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=0.35)\n",
    "embed.score_metagenes(adata, metagenes)\n",
    "embed.plot_metagenes_scores(adata, mgs, \"celltype\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "5b3d918c",
   "metadata": {},
   "source": [
    "## Step 5: Visualize network connectivity within desired gene program\n",
    "We can further visualize the connectivity between genes within any gene program of interest from Step 4. Here is an example of gene program 3 consisting of the CD3 cluster, CD8 cluster and other genes. In the visualization, we see strong connections highlighted in blue (by cosine similarity) between CD3D, E, and G, as well as CD8A and B."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5d1a61b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['LCK', 'CD2', 'LTB', 'CD3E', 'CD3G', 'CD8B', 'CD8A', 'IL7R', 'CD3D']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 9/9 [00:00<00:00, 254.81it/s]\n"
     ]
    }
   ],
   "source": [
    "# Retrieve gene program 3 which contains the CD3 gene set\n",
    "CD_genes = mgs['3']\n",
    "print(CD_genes)\n",
    "# Compute cosine similarities among genes in this gene program\n",
    "df_CD = pd.DataFrame(columns=['Gene', 'Similarity', 'Gene1'])\n",
    "for i in tqdm.tqdm(CD_genes):\n",
    "    df = embed.compute_similarities(i, CD_genes)\n",
    "    df['Gene1'] = i\n",
    "    df_CD = df_CD.append(df)\n",
    "df_CD_sub = df_CD[df_CD['Similarity']<0.99].sort_values(by='Gene') # Filter out edges from each gene to itself"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f7060ce1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creates a graph from the cosine similarity network\n",
    "input_node_weights = [(row['Gene'], row['Gene1'], round(row['Similarity'], 2)) for i, row in df_CD_sub.iterrows()]\n",
    "G = nx.Graph()\n",
    "G.add_weighted_edges_from(input_node_weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ff192716",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the cosine similarity network; strong edges (> select threshold) are highlighted\n",
    "thresh = 0.4\n",
    "plt.figure(figsize=(20, 20))\n",
    "widths = nx.get_edge_attributes(G, 'weight')\n",
    "\n",
    "elarge = [(u, v) for (u, v, d) in G.edges(data=True) if d[\"weight\"] > thresh]\n",
    "esmall = [(u, v) for (u, v, d) in G.edges(data=True) if d[\"weight\"] <= thresh]\n",
    "\n",
    "pos = nx.spring_layout(G, k=0.4, iterations=15, seed=3)\n",
    "\n",
    "width_large = {}\n",
    "width_small = {}\n",
    "for i, v in enumerate(list(widths.values())):\n",
    "    if v > thresh:\n",
    "        width_large[list(widths.keys())[i]] = v*10\n",
    "    else:\n",
    "        width_small[list(widths.keys())[i]] = max(v, 0)*10\n",
    "\n",
    "nx.draw_networkx_edges(G, pos,\n",
    "                       edgelist = width_small.keys(),\n",
    "                       width=list(width_small.values()),\n",
    "                       edge_color='lightblue',\n",
    "                       alpha=0.8)\n",
    "nx.draw_networkx_edges(G, pos, \n",
    "                       edgelist = width_large.keys(), \n",
    "                       width = list(width_large.values()), \n",
    "                       alpha = 0.5, \n",
    "                       edge_color = \"blue\", \n",
    "                      )\n",
    "# node labels\n",
    "nx.draw_networkx_labels(G, pos, font_size=25, font_family=\"sans-serif\")\n",
    "# edge weight labels\n",
    "d = nx.get_edge_attributes(G, \"weight\")\n",
    "edge_labels = {k: d[k] for k in elarge}\n",
    "nx.draw_networkx_edge_labels(G, pos, edge_labels, font_size=15)\n",
    "\n",
    "ax = plt.gca()\n",
    "ax.margins(0.08)\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ac302953",
   "metadata": {},
   "source": [
    "## Step 6: Reactome pathway analysis\n",
    "Again with gene program 3 as an example, users may perform pathway enrichment analysis to identify related pathways. In the paper, we used the Bonferroni correction to adjust the p-value threshold by accounting for the total number of tests performed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f3a07188",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Meta info about the number of terms (tests) in the databases\n",
    "df_database = pd.DataFrame(\n",
    "data = [['GO_Biological_Process_2021', 6036],\n",
    "['GO_Molecular_Function_2021', 1274],\n",
    "['Reactome_2022', 1818]],\n",
    "columns = ['dataset', 'term'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1a3e18c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select desired database for query; here use Reactome as an example\n",
    "databases = ['Reactome_2022']\n",
    "m = df_database[df_database['dataset'].isin(databases)]['term'].sum()\n",
    "# p-value correction for total number of tests done\n",
    "p_thresh = 0.05/m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7a1da728",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gene_set</th>\n",
       "      <th>Term</th>\n",
       "      <th>Overlap</th>\n",
       "      <th>P-value</th>\n",
       "      <th>Adjusted P-value</th>\n",
       "      <th>Old P-value</th>\n",
       "      <th>Old Adjusted P-value</th>\n",
       "      <th>Odds Ratio</th>\n",
       "      <th>Combined Score</th>\n",
       "      <th>Genes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Reactome_2022</td>\n",
       "      <td>Translocation Of ZAP-70 To Immunological Synap...</td>\n",
       "      <td>3/17</td>\n",
       "      <td>4.270574e-08</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>713.464286</td>\n",
       "      <td>12106.727222</td>\n",
       "      <td>LCK;CD3E;CD3D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Reactome_2022</td>\n",
       "      <td>Phosphorylation Of CD3 And TCR Zeta Chains R-H...</td>\n",
       "      <td>3/20</td>\n",
       "      <td>7.154717e-08</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>587.470588</td>\n",
       "      <td>9665.600052</td>\n",
       "      <td>LCK;CD3E;CD3D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Reactome_2022</td>\n",
       "      <td>PD-1 Signaling R-HSA-389948</td>\n",
       "      <td>3/21</td>\n",
       "      <td>8.345311e-08</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>554.805556</td>\n",
       "      <td>9042.765180</td>\n",
       "      <td>LCK;CD3E;CD3D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Reactome_2022</td>\n",
       "      <td>Immunoregulatory Interactions Between A Lympho...</td>\n",
       "      <td>4/123</td>\n",
       "      <td>1.675722e-07</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>133.593277</td>\n",
       "      <td>2084.302453</td>\n",
       "      <td>CD8B;CD8A;CD3E;CD3D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Reactome_2022</td>\n",
       "      <td>Generation Of Second Messenger Molecules R-HSA...</td>\n",
       "      <td>3/32</td>\n",
       "      <td>3.104597e-07</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>344.172414</td>\n",
       "      <td>5157.496482</td>\n",
       "      <td>LCK;CD3E;CD3D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Reactome_2022</td>\n",
       "      <td>Immune System R-HSA-168256</td>\n",
       "      <td>7/1943</td>\n",
       "      <td>2.439382e-06</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.640754</td>\n",
       "      <td>421.841460</td>\n",
       "      <td>CD8B;LCK;CD8A;LTB;CD3E;IL7R;CD3D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Reactome_2022</td>\n",
       "      <td>Costimulation By CD28 Family R-HSA-388841</td>\n",
       "      <td>3/68</td>\n",
       "      <td>3.111647e-06</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>153.276923</td>\n",
       "      <td>1943.606325</td>\n",
       "      <td>LCK;CD3E;CD3D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Reactome_2022</td>\n",
       "      <td>Adaptive Immune System R-HSA-1280218</td>\n",
       "      <td>5/733</td>\n",
       "      <td>7.270042e-06</td>\n",
       "      <td>0.000055</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>33.075206</td>\n",
       "      <td>391.337520</td>\n",
       "      <td>CD8B;LCK;CD8A;CD3E;CD3D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Reactome_2022</td>\n",
       "      <td>Downstream TCR Signaling R-HSA-202424</td>\n",
       "      <td>3/94</td>\n",
       "      <td>8.274085e-06</td>\n",
       "      <td>0.000055</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>109.340659</td>\n",
       "      <td>1279.546191</td>\n",
       "      <td>LCK;CD3E;CD3D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Reactome_2022</td>\n",
       "      <td>TCR Signaling R-HSA-202403</td>\n",
       "      <td>3/116</td>\n",
       "      <td>1.556787e-05</td>\n",
       "      <td>0.000093</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>87.955752</td>\n",
       "      <td>973.696687</td>\n",
       "      <td>LCK;CD3E;CD3D</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Gene_set                                               Term Overlap  \\\n",
       "0  Reactome_2022  Translocation Of ZAP-70 To Immunological Synap...    3/17   \n",
       "1  Reactome_2022  Phosphorylation Of CD3 And TCR Zeta Chains R-H...    3/20   \n",
       "2  Reactome_2022                        PD-1 Signaling R-HSA-389948    3/21   \n",
       "3  Reactome_2022  Immunoregulatory Interactions Between A Lympho...   4/123   \n",
       "4  Reactome_2022  Generation Of Second Messenger Molecules R-HSA...    3/32   \n",
       "5  Reactome_2022                         Immune System R-HSA-168256  7/1943   \n",
       "6  Reactome_2022          Costimulation By CD28 Family R-HSA-388841    3/68   \n",
       "7  Reactome_2022               Adaptive Immune System R-HSA-1280218   5/733   \n",
       "8  Reactome_2022              Downstream TCR Signaling R-HSA-202424    3/94   \n",
       "9  Reactome_2022                         TCR Signaling R-HSA-202403   3/116   \n",
       "\n",
       "        P-value  Adjusted P-value  Old P-value  Old Adjusted P-value  \\\n",
       "0  4.270574e-08          0.000002            0                     0   \n",
       "1  7.154717e-08          0.000002            0                     0   \n",
       "2  8.345311e-08          0.000002            0                     0   \n",
       "3  1.675722e-07          0.000003            0                     0   \n",
       "4  3.104597e-07          0.000004            0                     0   \n",
       "5  2.439382e-06          0.000024            0                     0   \n",
       "6  3.111647e-06          0.000027            0                     0   \n",
       "7  7.270042e-06          0.000055            0                     0   \n",
       "8  8.274085e-06          0.000055            0                     0   \n",
       "9  1.556787e-05          0.000093            0                     0   \n",
       "\n",
       "   Odds Ratio  Combined Score                             Genes  \n",
       "0  713.464286    12106.727222                     LCK;CD3E;CD3D  \n",
       "1  587.470588     9665.600052                     LCK;CD3E;CD3D  \n",
       "2  554.805556     9042.765180                     LCK;CD3E;CD3D  \n",
       "3  133.593277     2084.302453               CD8B;CD8A;CD3E;CD3D  \n",
       "4  344.172414     5157.496482                     LCK;CD3E;CD3D  \n",
       "5   32.640754      421.841460  CD8B;LCK;CD8A;LTB;CD3E;IL7R;CD3D  \n",
       "6  153.276923     1943.606325                     LCK;CD3E;CD3D  \n",
       "7   33.075206      391.337520           CD8B;LCK;CD8A;CD3E;CD3D  \n",
       "8  109.340659     1279.546191                     LCK;CD3E;CD3D  \n",
       "9   87.955752      973.696687                     LCK;CD3E;CD3D  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform pathway enrichment analysis using the gseapy package in the Reactome database\n",
    "df = pd.DataFrame()\n",
    "enr_Reactome = gp.enrichr(gene_list=CD_genes,\n",
    "                          gene_sets=databases,\n",
    "                          organism='Human', \n",
    "                          outdir='test/enr_Reactome',\n",
    "                          cutoff=0.5)\n",
    "out = enr_Reactome.results\n",
    "out = out[out['P-value'] < p_thresh]\n",
    "df = df.append(out, ignore_index=True)\n",
    "df"
   ]
  }
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